backend
backend on Beyond Market Intelligence: a running collection of 5 stories we have gathered and hand-picked because they are worth your time. Every post here touches on backend in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around backend, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
My Model Worked Perfectly. Then I Tried to Make It Useful.
Successfully deploying machine learning models can be deceptively challenging. Many data scientists achieve impressive accuracy in isolation, but translating that success into a practical, accessible service is a crucial next step. "My Model Worked Perfectly. Then I Tried to Make It Useful." details the journey of transforming a trained churn classifier into a robust FastAPI service—a vital component for integrating AI into broader software ecosystems.
Connecting My LangGraph AI Agent to Postgres
Connecting your LangGraph AI agent to a Postgres database unlocks powerful capabilities for data-driven workflows. This post details how to establish that connection, offering clear guidance for both local development and cloud deployment. We’ll explore setting up the backend using Docker for streamlined local testing, and then outline strategies for scaling to the cloud. For those tackling complex enterprise workflows, consider the recent exploration of an 8B AI model mirroring Claude Opus—a relevant challenge in managing substantial data sets.
Building a Proper Backend for My LangGraph AI Agent
Moving beyond demo agents, building a robust backend for your LangGraph AI agent is crucial for handling real-world data, like booking information. This post details the practical steps to transform a prototype into a reliable system capable of persistent storage and retrieval. We'll explore key architectural considerations and best practices for ensuring data integrity and scalability. For broader insights into building AI safety systems at scale, consider “Presentation: SafeChat,” which details DoorDash’s approach to content moderation.
Podcast: Rethinking Data: Moving From the Traditional Three-Tier Web Stack to Client-Side Event Sourcing
Johannes Schickling challenges conventional wisdom in our latest podcast, "Rethinking Data." He details his journey moving beyond the traditional three-tier web stack to a local-first architecture, sharing his experience building Overtone—a music curation app—with client-side event sourcing and SQLite. This episode unpacks the practical trade-offs inherent in event sourcing and CRDTs, offering valuable insights for developers seeking a more agile data management approach. For further exploration of evolving architectures, see our article, "An Evolutionary Architecture Pattern for Managing AI’s Pace of Change."
Stripe Benchmark Shows AI Agents Build Integrations but Struggle with Validation
Stripe’s new benchmark reveals a significant hurdle in the rise of AI agents: while capable of constructing Stripe integrations across key workflows, they consistently struggle with validation. This suite assesses end-to-end software engineering capabilities, highlighting critical gaps in execution, testing, and validation—particularly under production-like conditions. The findings underscore that achieving reliable agentic systems requires focused improvements beyond initial build phases. For deeper insights into a related challenge, explore "Most RAG Hallucinations Are Retrieval Failures" to understand how data retrieval impacts AI accuracy.